Overview

Dataset statistics

Number of variables22
Number of observations218499
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory33.8 MiB
Average record size in memory162.0 B

Variable types

Numeric9
Text6
DateTime2
Categorical3
Boolean2

Alerts

Year has constant value ""Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
ID is highly overall correlated with Primary Type and 1 other fieldsHigh correlation
Beat is highly overall correlated with District and 6 other fieldsHigh correlation
District is highly overall correlated with Beat and 6 other fieldsHigh correlation
Ward is highly overall correlated with Beat and 4 other fieldsHigh correlation
Community Area is highly overall correlated with Beat and 4 other fieldsHigh correlation
X Coordinate is highly overall correlated with Beat and 4 other fieldsHigh correlation
Y Coordinate is highly overall correlated with Beat and 6 other fieldsHigh correlation
Latitude is highly overall correlated with Beat and 6 other fieldsHigh correlation
Longitude is highly overall correlated with Beat and 4 other fieldsHigh correlation
Primary Type is highly overall correlated with ID and 3 other fieldsHigh correlation
Arrest is highly overall correlated with Primary Type and 1 other fieldsHigh correlation
Domestic is highly overall correlated with Primary Type and 1 other fieldsHigh correlation
FBI Code is highly overall correlated with ID and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-07-16 16:06:17.160008
Analysis finished2023-07-16 16:07:56.674368
Duration1 minute and 39.51 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION 

Distinct218497
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12715094
Minimum26543
Maximum12921045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:37:56.919283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26543
5-th percentile12600506
Q112671340
median12755264
Q312836732
95-th percentile12901047
Maximum12921045
Range12894502
Interquartile range (IQR)165392

Descriptive statistics

Standard deviation708586.23
Coefficient of variation (CV)0.055727959
Kurtosis310.84936
Mean12715094
Median Absolute Deviation (MAD)82686
Skewness-17.523836
Sum2.7782353 × 1012
Variance5.0209445 × 1011
MonotonicityNot monotonic
2023-07-16T21:37:57.265326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12582764 2
 
< 0.1%
12582274 2
 
< 0.1%
12789250 1
 
< 0.1%
12806301 1
 
< 0.1%
12806263 1
 
< 0.1%
12806342 1
 
< 0.1%
12809715 1
 
< 0.1%
12806221 1
 
< 0.1%
12806555 1
 
< 0.1%
12806271 1
 
< 0.1%
Other values (218487) 218487
> 99.9%
ValueCountFrequency (%)
26543 1
< 0.1%
26544 1
< 0.1%
26545 1
< 0.1%
26546 1
< 0.1%
26547 1
< 0.1%
26548 1
< 0.1%
26549 1
< 0.1%
26550 1
< 0.1%
26551 1
< 0.1%
26552 1
< 0.1%
ValueCountFrequency (%)
12921045 1
< 0.1%
12921044 1
< 0.1%
12921016 1
< 0.1%
12921012 1
< 0.1%
12921005 1
< 0.1%
12921001 1
< 0.1%
12920997 1
< 0.1%
12920996 1
< 0.1%
12920992 1
< 0.1%
12920991 1
< 0.1%
Distinct218457
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-07-16T21:37:57.990084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1747992
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218418 ?
Unique (%)> 99.9%

Sample

1st rowJF351068
2nd rowJF343908
3rd rowJF380963
4th rowJF389504
5th rowJF403613
ValueCountFrequency (%)
jf356096 3
 
< 0.1%
jf445443 3
 
< 0.1%
jf198311 3
 
< 0.1%
jf383204 2
 
< 0.1%
jf390178 2
 
< 0.1%
jf167557 2
 
< 0.1%
jf126040 2
 
< 0.1%
jf259064 2
 
< 0.1%
jf496175 2
 
< 0.1%
jf203921 2
 
< 0.1%
Other values (218447) 218476
> 99.9%
2023-07-16T21:37:58.874621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
J 218496
12.5%
F 218478
12.5%
4 166146
9.5%
3 165061
9.4%
2 163026
9.3%
1 159418
9.1%
5 111173
6.4%
0 110594
6.3%
6 109254
6.3%
9 109094
6.2%
Other values (9) 217252
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310994
75.0%
Uppercase Letter 436998
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 166146
12.7%
3 165061
12.6%
2 163026
12.4%
1 159418
12.2%
5 111173
8.5%
0 110594
8.4%
6 109254
8.3%
9 109094
8.3%
8 108839
8.3%
7 108389
8.3%
Uppercase Letter
ValueCountFrequency (%)
J 218496
50.0%
F 218478
50.0%
E 13
 
< 0.1%
H 4
 
< 0.1%
D 3
 
< 0.1%
P 1
 
< 0.1%
A 1
 
< 0.1%
V 1
 
< 0.1%
R 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1310994
75.0%
Latin 436998
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 166146
12.7%
3 165061
12.6%
2 163026
12.4%
1 159418
12.2%
5 111173
8.5%
0 110594
8.4%
6 109254
8.3%
9 109094
8.3%
8 108839
8.3%
7 108389
8.3%
Latin
ValueCountFrequency (%)
J 218496
50.0%
F 218478
50.0%
E 13
 
< 0.1%
H 4
 
< 0.1%
D 3
 
< 0.1%
P 1
 
< 0.1%
A 1
 
< 0.1%
V 1
 
< 0.1%
R 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1747992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J 218496
12.5%
F 218478
12.5%
4 166146
9.5%
3 165061
9.4%
2 163026
9.3%
1 159418
9.1%
5 111173
6.4%
0 110594
6.3%
6 109254
6.3%
9 109094
6.2%
Other values (9) 217252
12.4%

Date
Date

Distinct103846
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Minimum2022-01-01 00:00:00
Maximum2023-01-01 12:37:00
2023-07-16T21:37:59.221439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:59.525129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Block
Text

Distinct27225
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-07-16T21:37:59.935410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length28
Mean length18.499426
Min length14

Characters and Unicode

Total characters4042106
Distinct characters62
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5077 ?
Unique (%)2.3%

Sample

1st row079XX S ST LAWRENCE AVE
2nd row007XX N MICHIGAN AVE
3rd row047XX W WABANSIA AVE
4th row039XX N PINE GROVE AVE
5th row042XX W WASHINGTON BLVD
ValueCountFrequency (%)
ave 112297
 
12.5%
s 86704
 
9.7%
st 76739
 
8.6%
w 66980
 
7.5%
n 48612
 
5.4%
e 16668
 
1.9%
dr 8132
 
0.9%
blvd 7687
 
0.9%
rd 7174
 
0.8%
0000x 7087
 
0.8%
Other values (1385) 456782
51.0%
2023-07-16T21:38:00.697077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
676363
16.7%
X 433182
 
10.7%
0 292897
 
7.2%
E 274318
 
6.8%
A 257828
 
6.4%
S 236357
 
5.8%
T 170818
 
4.2%
N 166361
 
4.1%
V 133824
 
3.3%
R 129386
 
3.2%
Other values (52) 1270772
31.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2633259
65.1%
Decimal Number 725805
 
18.0%
Space Separator 676363
 
16.7%
Lowercase Letter 6679
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 433182
16.5%
E 274318
10.4%
A 257828
9.8%
S 236357
 
9.0%
T 170818
 
6.5%
N 166361
 
6.3%
V 133824
 
5.1%
R 129386
 
4.9%
L 113268
 
4.3%
W 98936
 
3.8%
Other values (16) 618981
23.5%
Lowercase Letter
ValueCountFrequency (%)
e 988
14.8%
a 677
10.1%
t 565
 
8.5%
n 520
 
7.8%
v 494
 
7.4%
r 479
 
7.2%
o 417
 
6.2%
i 409
 
6.1%
l 407
 
6.1%
d 293
 
4.4%
Other values (15) 1430
21.4%
Decimal Number
ValueCountFrequency (%)
0 292897
40.4%
1 82584
 
11.4%
2 52128
 
7.2%
3 51038
 
7.0%
5 47217
 
6.5%
4 46745
 
6.4%
7 44958
 
6.2%
6 44727
 
6.2%
8 34162
 
4.7%
9 29349
 
4.0%
Space Separator
ValueCountFrequency (%)
676363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2639938
65.3%
Common 1402168
34.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 433182
16.4%
E 274318
10.4%
A 257828
9.8%
S 236357
 
9.0%
T 170818
 
6.5%
N 166361
 
6.3%
V 133824
 
5.1%
R 129386
 
4.9%
L 113268
 
4.3%
W 98936
 
3.7%
Other values (41) 625660
23.7%
Common
ValueCountFrequency (%)
676363
48.2%
0 292897
20.9%
1 82584
 
5.9%
2 52128
 
3.7%
3 51038
 
3.6%
5 47217
 
3.4%
4 46745
 
3.3%
7 44958
 
3.2%
6 44727
 
3.2%
8 34162
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4042106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
676363
16.7%
X 433182
 
10.7%
0 292897
 
7.2%
E 274318
 
6.8%
A 257828
 
6.4%
S 236357
 
5.8%
T 170818
 
4.2%
N 166361
 
4.1%
V 133824
 
3.3%
R 129386
 
3.2%
Other values (52) 1270772
31.4%

IUCR
Text

Distinct298
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:01.229640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.380766
Min length3

Characters and Unicode

Total characters738694
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st row810
2nd row580
3rd row1563
4th row1153
5th row1750
ValueCountFrequency (%)
810 18449
 
8.4%
486 17502
 
8.0%
820 17312
 
7.9%
910 15534
 
7.1%
1320 13074
 
6.0%
460 12843
 
5.9%
560 12411
 
5.7%
1310 11393
 
5.2%
860 8037
 
3.7%
143a 5150
 
2.4%
Other values (288) 86794
39.7%
2023-07-16T21:38:02.092139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 169971
23.0%
1 136777
18.5%
8 82849
11.2%
2 72456
9.8%
6 65951
 
8.9%
3 58373
 
7.9%
4 53995
 
7.3%
5 41082
 
5.6%
9 27199
 
3.7%
A 15516
 
2.1%
Other values (8) 14525
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 721202
97.6%
Uppercase Letter 17492
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169971
23.6%
1 136777
19.0%
8 82849
11.5%
2 72456
10.0%
6 65951
 
9.1%
3 58373
 
8.1%
4 53995
 
7.5%
5 41082
 
5.7%
9 27199
 
3.8%
7 12549
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
A 15516
88.7%
P 757
 
4.3%
B 618
 
3.5%
R 462
 
2.6%
C 110
 
0.6%
T 16
 
0.1%
N 11
 
0.1%
E 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 721202
97.6%
Latin 17492
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169971
23.6%
1 136777
19.0%
8 82849
11.5%
2 72456
10.0%
6 65951
 
9.1%
3 58373
 
8.1%
4 53995
 
7.5%
5 41082
 
5.7%
9 27199
 
3.8%
7 12549
 
1.7%
Latin
ValueCountFrequency (%)
A 15516
88.7%
P 757
 
4.3%
B 618
 
3.5%
R 462
 
2.6%
C 110
 
0.6%
T 16
 
0.1%
N 11
 
0.1%
E 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 738694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169971
23.0%
1 136777
18.5%
8 82849
11.2%
2 72456
9.8%
6 65951
 
8.9%
3 58373
 
7.9%
4 53995
 
7.3%
5 41082
 
5.6%
9 27199
 
3.7%
A 15516
 
2.1%
Other values (8) 14525
 
2.0%

Primary Type
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
THEFT
50072 
BATTERY
38447 
CRIMINAL DAMAGE
25237 
ASSAULT
19561 
MOTOR VEHICLE THEFT
19270 
Other values (26)
65912 

Length

Max length33
Median length32
Mean length10.60717
Min length5

Characters and Unicode

Total characters2317656
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHEFT
2nd rowSTALKING
3rd rowSEX OFFENSE
4th rowDECEPTIVE PRACTICE
5th rowOFFENSE INVOLVING CHILDREN

Common Values

ValueCountFrequency (%)
THEFT 50072
22.9%
BATTERY 38447
17.6%
CRIMINAL DAMAGE 25237
11.6%
ASSAULT 19561
 
9.0%
MOTOR VEHICLE THEFT 19270
 
8.8%
OTHER OFFENSE 13481
 
6.2%
DECEPTIVE PRACTICE 13351
 
6.1%
ROBBERY 8299
 
3.8%
WEAPONS VIOLATION 8287
 
3.8%
BURGLARY 7024
 
3.2%
Other values (21) 15470
 
7.1%

Length

2023-07-16T21:38:02.494990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
theft 69342
20.9%
battery 38447
11.6%
criminal 30624
9.2%
damage 25237
 
7.6%
assault 21002
 
6.3%
motor 19270
 
5.8%
vehicle 19270
 
5.8%
offense 16370
 
4.9%
other 13486
 
4.1%
practice 13351
 
4.0%
Other values (34) 65865
19.8%

Most occurring characters

ValueCountFrequency (%)
T 315370
13.6%
E 300661
13.0%
A 210921
 
9.1%
R 157609
 
6.8%
I 140671
 
6.1%
113765
 
4.9%
O 111668
 
4.8%
H 104935
 
4.5%
F 103245
 
4.5%
C 103021
 
4.4%
Other values (16) 655790
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2203887
95.1%
Space Separator 113765
 
4.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 315370
14.3%
E 300661
13.6%
A 210921
 
9.6%
R 157609
 
7.2%
I 140671
 
6.4%
O 111668
 
5.1%
H 104935
 
4.8%
F 103245
 
4.7%
C 103021
 
4.7%
L 94394
 
4.3%
Other values (14) 561392
25.5%
Space Separator
ValueCountFrequency (%)
113765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2203887
95.1%
Common 113769
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 315370
14.3%
E 300661
13.6%
A 210921
 
9.6%
R 157609
 
7.2%
I 140671
 
6.4%
O 111668
 
5.1%
H 104935
 
4.8%
F 103245
 
4.7%
C 103021
 
4.7%
L 94394
 
4.3%
Other values (14) 561392
25.5%
Common
ValueCountFrequency (%)
113765
> 99.9%
- 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2317656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 315370
13.6%
E 300661
13.0%
A 210921
 
9.1%
R 157609
 
6.8%
I 140671
 
6.1%
113765
 
4.9%
O 111668
 
4.8%
H 104935
 
4.5%
F 103245
 
4.5%
C 103021
 
4.4%
Other values (16) 655790
28.3%
Distinct278
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:02.882759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length55
Mean length16.619174
Min length5

Characters and Unicode

Total characters3631273
Distinct characters39
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowOVER $500
2nd rowSIMPLE
3rd rowCRIMINAL SEXUAL ABUSE
4th rowFINANCIAL IDENTITY THEFT OVER $ 300
5th rowCHILD ABUSE
ValueCountFrequency (%)
47050
 
8.1%
simple 43021
 
7.4%
500 35761
 
6.2%
to 29081
 
5.0%
over 21021
 
3.6%
domestic 19667
 
3.4%
battery 19639
 
3.4%
automobile 18792
 
3.3%
under 18462
 
3.2%
and 18434
 
3.2%
Other values (343) 307249
53.1%
2023-07-16T21:38:03.725087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 392382
 
10.8%
359877
 
9.9%
T 277360
 
7.6%
A 234658
 
6.5%
R 219410
 
6.0%
O 216363
 
6.0%
I 208549
 
5.7%
N 190635
 
5.2%
S 156337
 
4.3%
L 150451
 
4.1%
Other values (29) 1225251
33.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3052866
84.1%
Space Separator 359877
 
9.9%
Decimal Number 119818
 
3.3%
Currency Symbol 39182
 
1.1%
Dash Punctuation 38077
 
1.0%
Other Punctuation 19505
 
0.5%
Open Punctuation 974
 
< 0.1%
Close Punctuation 974
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 392382
12.9%
T 277360
 
9.1%
A 234658
 
7.7%
R 219410
 
7.2%
O 216363
 
7.1%
I 208549
 
6.8%
N 190635
 
6.2%
S 156337
 
5.1%
L 150451
 
4.9%
D 136473
 
4.5%
Other values (16) 870248
28.5%
Decimal Number
ValueCountFrequency (%)
0 79499
66.3%
5 35761
29.8%
3 4249
 
3.5%
1 308
 
0.3%
8 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 9734
49.9%
, 8031
41.2%
. 1740
 
8.9%
Space Separator
ValueCountFrequency (%)
359877
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 39182
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38077
100.0%
Open Punctuation
ValueCountFrequency (%)
( 974
100.0%
Close Punctuation
ValueCountFrequency (%)
) 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3052866
84.1%
Common 578407
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 392382
12.9%
T 277360
 
9.1%
A 234658
 
7.7%
R 219410
 
7.2%
O 216363
 
7.1%
I 208549
 
6.8%
N 190635
 
6.2%
S 156337
 
5.1%
L 150451
 
4.9%
D 136473
 
4.5%
Other values (16) 870248
28.5%
Common
ValueCountFrequency (%)
359877
62.2%
0 79499
 
13.7%
$ 39182
 
6.8%
- 38077
 
6.6%
5 35761
 
6.2%
/ 9734
 
1.7%
, 8031
 
1.4%
3 4249
 
0.7%
. 1740
 
0.3%
( 974
 
0.2%
Other values (3) 1283
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3631273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 392382
 
10.8%
359877
 
9.9%
T 277360
 
7.6%
A 234658
 
6.5%
R 219410
 
6.0%
O 216363
 
6.0%
I 208549
 
5.7%
N 190635
 
5.2%
S 156337
 
4.3%
L 150451
 
4.1%
Other values (29) 1225251
33.7%
Distinct132
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:04.094493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length47
Mean length11.922064
Min length4

Characters and Unicode

Total characters2604959
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowRESIDENCE
2nd rowSTREET
3rd rowRESIDENCE - GARAGE
4th rowAPARTMENT
5th rowRESIDENCE
ValueCountFrequency (%)
street 62634
16.2%
apartment 42331
 
11.0%
38466
 
10.0%
residence 35419
 
9.2%
store 15048
 
3.9%
sidewalk 11338
 
2.9%
garage 11255
 
2.9%
lot 10432
 
2.7%
parking 9834
 
2.6%
residential 9379
 
2.4%
Other values (172) 139477
36.2%
2023-07-16T21:38:04.783972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 409930
15.7%
T 302793
11.6%
R 259772
10.0%
A 218923
 
8.4%
S 178133
 
6.8%
167114
 
6.4%
N 160721
 
6.2%
I 130505
 
5.0%
L 99035
 
3.8%
O 93069
 
3.6%
Other values (25) 584964
22.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2367050
90.9%
Space Separator 167114
 
6.4%
Other Punctuation 25329
 
1.0%
Dash Punctuation 16944
 
0.7%
Close Punctuation 14261
 
0.5%
Open Punctuation 14261
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 409930
17.3%
T 302793
12.8%
R 259772
11.0%
A 218923
9.2%
S 178133
7.5%
N 160721
 
6.8%
I 130505
 
5.5%
L 99035
 
4.2%
O 93069
 
3.9%
C 86447
 
3.7%
Other values (16) 427722
18.1%
Other Punctuation
ValueCountFrequency (%)
/ 25067
99.0%
, 162
 
0.6%
. 84
 
0.3%
: 8
 
< 0.1%
" 8
 
< 0.1%
Space Separator
ValueCountFrequency (%)
167114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16944
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14261
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2367050
90.9%
Common 237909
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 409930
17.3%
T 302793
12.8%
R 259772
11.0%
A 218923
9.2%
S 178133
7.5%
N 160721
 
6.8%
I 130505
 
5.5%
L 99035
 
4.2%
O 93069
 
3.9%
C 86447
 
3.7%
Other values (16) 427722
18.1%
Common
ValueCountFrequency (%)
167114
70.2%
/ 25067
 
10.5%
- 16944
 
7.1%
) 14261
 
6.0%
( 14261
 
6.0%
, 162
 
0.1%
. 84
 
< 0.1%
: 8
 
< 0.1%
" 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2604959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 409930
15.7%
T 302793
11.6%
R 259772
10.0%
A 218923
 
8.4%
S 178133
 
6.8%
167114
 
6.4%
N 160721
 
6.2%
I 130505
 
5.0%
L 99035
 
3.8%
O 93069
 
3.6%
Other values (25) 584964
22.5%

Arrest
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
False
193899 
True
24600 
ValueCountFrequency (%)
False 193899
88.7%
True 24600
 
11.3%
2023-07-16T21:38:05.125724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Domestic
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.5 KiB
False
178814 
True
39685 
ValueCountFrequency (%)
False 178814
81.8%
True 39685
 
18.2%
2023-07-16T21:38:05.366707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Beat
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1151.0805
Minimum111
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:05.681429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile133
Q1533
median1031
Q31724
95-th percentile2511
Maximum2535
Range2424
Interquartile range (IQR)1191

Descriptive statistics

Standard deviation707.56041
Coefficient of variation (CV)0.61469239
Kurtosis-0.96472624
Mean1151.0805
Median Absolute Deviation (MAD)593
Skewness0.36904328
Sum2.5150994 × 108
Variance500641.73
MonotonicityNot monotonic
2023-07-16T21:38:06.078528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1834 2810
 
1.3%
123 1942
 
0.9%
421 1935
 
0.9%
1831 1750
 
0.8%
511 1620
 
0.7%
423 1611
 
0.7%
631 1578
 
0.7%
624 1556
 
0.7%
414 1452
 
0.7%
1533 1430
 
0.7%
Other values (264) 200815
91.9%
ValueCountFrequency (%)
111 1263
0.6%
112 1078
0.5%
113 622
 
0.3%
114 1186
0.5%
121 556
 
0.3%
122 1186
0.5%
123 1942
0.9%
124 812
0.4%
131 1353
0.6%
132 836
0.4%
ValueCountFrequency (%)
2535 690
0.3%
2534 935
0.4%
2533 1112
0.5%
2532 908
0.4%
2531 584
0.3%
2525 413
 
0.2%
2524 494
0.2%
2523 547
0.3%
2522 645
0.3%
2521 983
0.4%

District
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.280798
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:06.364528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q317
95-th percentile25
Maximum31
Range30
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0715344
Coefficient of variation (CV)0.62686476
Kurtosis-0.96045862
Mean11.280798
Median Absolute Deviation (MAD)6
Skewness0.37176934
Sum2464843
Variance50.006599
MonotonicityNot monotonic
2023-07-16T21:38:06.649610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 13598
 
6.2%
6 13592
 
6.2%
12 13038
 
6.0%
4 12877
 
5.9%
11 12073
 
5.5%
1 11812
 
5.4%
18 11298
 
5.2%
19 11026
 
5.0%
3 11015
 
5.0%
25 10927
 
5.0%
Other values (13) 97243
44.5%
ValueCountFrequency (%)
1 11812
5.4%
2 10727
4.9%
3 11015
5.0%
4 12877
5.9%
5 9091
4.2%
6 13592
6.2%
7 9593
4.4%
8 13598
6.2%
9 9566
4.4%
10 9278
4.2%
ValueCountFrequency (%)
31 8
 
< 0.1%
25 10927
5.0%
24 7748
3.5%
22 7050
3.2%
20 4499
 
2.1%
19 11026
5.0%
18 11298
5.2%
17 6344
2.9%
16 8128
3.7%
15 7786
3.6%

Ward
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.309297
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:06.948589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median24
Q335
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.194613
Coefficient of variation (CV)0.60896787
Kurtosis-1.1433778
Mean23.309297
Median Absolute Deviation (MAD)13
Skewness0.15050148
Sum5093058
Variance201.48703
MonotonicityNot monotonic
2023-07-16T21:38:07.274144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 11576
 
5.3%
27 10097
 
4.6%
28 9290
 
4.3%
6 7928
 
3.6%
24 7492
 
3.4%
4 7053
 
3.2%
7 7039
 
3.2%
3 6907
 
3.2%
8 6851
 
3.1%
21 6754
 
3.1%
Other values (40) 137512
62.9%
ValueCountFrequency (%)
1 4043
1.9%
2 3449
1.6%
3 6907
3.2%
4 7053
3.2%
5 5626
2.6%
6 7928
3.6%
7 7039
3.2%
8 6851
3.1%
9 6293
2.9%
10 3675
1.7%
ValueCountFrequency (%)
50 2796
 
1.3%
49 3704
 
1.7%
48 2706
 
1.2%
47 2313
 
1.1%
46 3483
 
1.6%
45 2450
 
1.1%
44 3486
 
1.6%
43 2449
 
1.1%
42 11576
5.3%
41 2741
 
1.3%

Community Area
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.37299
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:07.600147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q122
median32
Q353
95-th percentile71
Maximum77
Range76
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.529494
Coefficient of variation (CV)0.59190884
Kurtosis-1.0219451
Mean36.37299
Median Absolute Deviation (MAD)16
Skewness0.22518399
Sum7947462
Variance463.51913
MonotonicityNot monotonic
2023-07-16T21:38:08.009280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 11435
 
5.2%
8 9629
 
4.4%
28 8624
 
3.9%
43 7723
 
3.5%
32 7278
 
3.3%
24 6578
 
3.0%
29 6141
 
2.8%
71 5815
 
2.7%
44 5615
 
2.6%
23 5604
 
2.6%
Other values (67) 144057
65.9%
ValueCountFrequency (%)
1 3711
 
1.7%
2 3621
 
1.7%
3 3514
 
1.6%
4 1801
 
0.8%
5 1176
 
0.5%
6 5279
2.4%
7 3224
 
1.5%
8 9629
4.4%
9 274
 
0.1%
10 1140
 
0.5%
ValueCountFrequency (%)
77 2616
1.2%
76 1523
 
0.7%
75 1606
 
0.7%
74 493
 
0.2%
73 2691
1.2%
72 825
 
0.4%
71 5815
2.7%
70 1841
 
0.8%
69 5534
2.5%
68 4360
2.0%

FBI Code
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
6
50072 
08B
31851 
14
25237 
7
19270 
08A
16475 
Other values (21)
75594 

Length

Max length3
Median length2
Mean length1.891432
Min length1

Characters and Unicode

Total characters413276
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row08A
3rd row17
4th row11
5th row08B

Common Values

ValueCountFrequency (%)
6 50072
22.9%
08B 31851
14.6%
14 25237
11.6%
7 19270
 
8.8%
08A 16475
 
7.5%
26 14422
 
6.6%
11 11132
 
5.1%
15 8459
 
3.9%
3 8299
 
3.8%
04B 7114
 
3.3%
Other values (16) 26168
12.0%

Length

2023-07-16T21:38:08.362457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6 50072
22.9%
08b 31851
14.6%
14 25237
11.6%
7 19270
 
8.8%
08a 16475
 
7.5%
26 14422
 
6.6%
11 11132
 
5.1%
15 8459
 
3.9%
3 8299
 
3.8%
04b 7114
 
3.3%
Other values (16) 26168
12.0%

Most occurring characters

ValueCountFrequency (%)
0 65736
15.9%
6 64772
15.7%
1 64381
15.6%
8 52036
12.6%
4 40226
9.7%
B 38969
9.4%
A 23991
 
5.8%
7 20802
 
5.0%
2 18112
 
4.4%
5 15483
 
3.7%
Other values (2) 8768
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 350316
84.8%
Uppercase Letter 62960
 
15.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65736
18.8%
6 64772
18.5%
1 64381
18.4%
8 52036
14.9%
4 40226
11.5%
7 20802
 
5.9%
2 18112
 
5.2%
5 15483
 
4.4%
3 8365
 
2.4%
9 403
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 38969
61.9%
A 23991
38.1%

Most occurring scripts

ValueCountFrequency (%)
Common 350316
84.8%
Latin 62960
 
15.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65736
18.8%
6 64772
18.5%
1 64381
18.4%
8 52036
14.9%
4 40226
11.5%
7 20802
 
5.9%
2 18112
 
5.2%
5 15483
 
4.4%
3 8365
 
2.4%
9 403
 
0.1%
Latin
ValueCountFrequency (%)
B 38969
61.9%
A 23991
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65736
15.9%
6 64772
15.7%
1 64381
15.6%
8 52036
12.6%
4 40226
9.7%
B 38969
9.4%
A 23991
 
5.8%
7 20802
 
5.0%
2 18112
 
4.4%
5 15483
 
3.7%
Other values (2) 8768
 
2.1%

X Coordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct48780
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1165408.4
Minimum0
Maximum1205119
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:08.682605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1138058
Q11153973
median1167294
Q31176876
95-th percentile1191755.4
Maximum1205119
Range1205119
Interquartile range (IQR)22903

Descriptive statistics

Standard deviation16793.183
Coefficient of variation (CV)0.014409698
Kurtosis212.39068
Mean1165408.4
Median Absolute Deviation (MAD)10905
Skewness-3.4498196
Sum2.5464056 × 1011
Variance2.8201099 × 108
MonotonicityNot monotonic
2023-07-16T21:38:09.163608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1176352 358
 
0.2%
1100658 284
 
0.1%
1178722 253
 
0.1%
1145654 209
 
0.1%
1176963 208
 
0.1%
1171818 193
 
0.1%
1101811 186
 
0.1%
1164808 183
 
0.1%
1178046 158
 
0.1%
1175696 157
 
0.1%
Other values (48770) 216310
99.0%
ValueCountFrequency (%)
0 2
 
< 0.1%
1091242 2
 
< 0.1%
1094470 2
 
< 0.1%
1095842 1
 
< 0.1%
1097306 1
 
< 0.1%
1098012 9
< 0.1%
1098141 2
 
< 0.1%
1098226 1
 
< 0.1%
1098287 1
 
< 0.1%
1099408 1
 
< 0.1%
ValueCountFrequency (%)
1205119 2
 
< 0.1%
1205117 1
 
< 0.1%
1205116 4
 
< 0.1%
1205114 2
 
< 0.1%
1205109 1
 
< 0.1%
1205026 1
 
< 0.1%
1204937 1
 
< 0.1%
1204911 10
< 0.1%
1204886 1
 
< 0.1%
1204873 3
 
< 0.1%

Y Coordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct67167
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1886937.9
Minimum0
Maximum1951493
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:09.434164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1835276
Q11859243.5
median1893230
Q31909911.5
95-th percentile1937424
Maximum1951493
Range1951493
Interquartile range (IQR)50668

Descriptive statistics

Standard deviation32311.178
Coefficient of variation (CV)0.017123604
Kurtosis105.38907
Mean1886937.9
Median Absolute Deviation (MAD)26428
Skewness-1.8971447
Sum4.1229405 × 1011
Variance1.0440122 × 109
MonotonicityNot monotonic
2023-07-16T21:38:09.735920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1934241 284
 
0.1%
1900927 261
 
0.1%
1901251 256
 
0.1%
1866253 208
 
0.1%
1918166 194
 
0.1%
1934419 190
 
0.1%
1905133 187
 
0.1%
1908052 177
 
0.1%
1894850 163
 
0.1%
1895352 155
 
0.1%
Other values (67157) 216424
99.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1813909 2
 
< 0.1%
1813938 1
 
< 0.1%
1813943 1
 
< 0.1%
1814105 1
 
< 0.1%
1814333 2
 
< 0.1%
1814359 1
 
< 0.1%
1814476 2
 
< 0.1%
1814512 5
< 0.1%
1814534 1
 
< 0.1%
ValueCountFrequency (%)
1951493 27
< 0.1%
1951492 26
< 0.1%
1951424 1
 
< 0.1%
1951413 1
 
< 0.1%
1951400 5
 
< 0.1%
1951399 3
 
< 0.1%
1951397 2
 
< 0.1%
1951396 2
 
< 0.1%
1951318 20
< 0.1%
1951301 3
 
< 0.1%

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2022
218499 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters873996
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 218499
100.0%

Length

2023-07-16T21:38:10.058074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T21:38:10.323332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 218499
100.0%

Most occurring characters

ValueCountFrequency (%)
2 655497
75.0%
0 218499
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 873996
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 655497
75.0%
0 218499
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 873996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 655497
75.0%
0 218499
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 873996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 655497
75.0%
0 218499
 
25.0%
Distinct140
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Minimum2022-02-09 15:53:00
Maximum2022-12-14 15:49:33
2023-07-16T21:38:10.605531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:38:10.949612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct111988
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.845336
Minimum36.619446
Maximum42.022548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:11.290991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.619446
5-th percentile41.703018
Q141.769013
median41.86283
Q341.908499
95-th percentile41.984109
Maximum42.022548
Range5.4031012
Interquartile range (IQR)0.13948526

Descriptive statistics

Standard deviation0.088876569
Coefficient of variation (CV)0.0021239301
Kurtosis108.33606
Mean41.845336
Median Absolute Deviation (MAD)0.07260702
Skewness-1.9355391
Sum9143164.1
Variance0.0078990445
MonotonicityNot monotonic
2023-07-16T21:38:11.627767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.97629041 284
 
0.1%
41.88350019 261
 
0.1%
41.88433547 253
 
0.1%
41.78898704 208
 
0.1%
41.930906 189
 
0.1%
41.97676298 186
 
0.1%
41.9033043 176
 
0.1%
41.86681074 163
 
0.1%
41.89500328 157
 
0.1%
41.86821682 155
 
0.1%
Other values (111978) 216467
99.1%
ValueCountFrequency (%)
36.6194464 2
 
< 0.1%
41.64459516 1
 
< 0.1%
41.64460828 2
 
< 0.1%
41.64461202 1
 
< 0.1%
41.64515591 1
 
< 0.1%
41.64528766 2
 
< 0.1%
41.64579585 5
< 0.1%
41.64585884 1
 
< 0.1%
41.64618709 2
 
< 0.1%
41.64634313 1
 
< 0.1%
ValueCountFrequency (%)
42.02254757 3
< 0.1%
42.02253659 2
< 0.1%
42.02253638 1
 
< 0.1%
42.02253615 2
< 0.1%
42.02253591 3
< 0.1%
42.0225357 3
< 0.1%
42.02253547 1
 
< 0.1%
42.02253503 2
< 0.1%
42.02253479 2
< 0.1%
42.02253458 3
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct111879
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.6685
Minimum-91.686566
Maximum-87.524532
Zeros0
Zeros (%)0.0%
Negative218499
Negative (%)100.0%
Memory size1.7 MiB
2023-07-16T21:38:11.943313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-91.686566
5-th percentile-87.768299
Q1-87.710081
median-87.661317
Q3-87.626358
95-th percentile-87.572803
Maximum-87.524532
Range4.1620341
Interquartile range (IQR)0.08372312

Descriptive statistics

Standard deviation0.06099773
Coefficient of variation (CV)-0.00069577704
Kurtosis172.46951
Mean-87.6685
Median Absolute Deviation (MAD)0.03963635
Skewness-3.0222254
Sum-19155480
Variance0.003720723
MonotonicityNot monotonic
2023-07-16T21:38:12.295829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.90522722 284
 
0.1%
-87.6278767 261
 
0.1%
-87.61916409 253
 
0.1%
-87.74147999 208
 
0.1%
-87.64401704 189
 
0.1%
-87.90098372 186
 
0.1%
-87.67006468 176
 
0.1%
-87.62581703 163
 
0.1%
-87.62152816 157
 
0.1%
-87.6304532 155
 
0.1%
Other values (111869) 216467
99.1%
ValueCountFrequency (%)
-91.68656568 2
 
< 0.1%
-87.93973294 2
 
< 0.1%
-87.92788174 2
 
< 0.1%
-87.92304357 1
 
< 0.1%
-87.91764464 1
 
< 0.1%
-87.91510545 9
< 0.1%
-87.91458534 2
 
< 0.1%
-87.91404562 1
 
< 0.1%
-87.91403057 1
 
< 0.1%
-87.90990869 1
 
< 0.1%
ValueCountFrequency (%)
-87.52453156 1
 
< 0.1%
-87.52453172 4
 
< 0.1%
-87.52454762 1
 
< 0.1%
-87.5245481 1
 
< 0.1%
-87.52462635 1
 
< 0.1%
-87.52465043 1
 
< 0.1%
-87.52465136 1
 
< 0.1%
-87.52527014 1
 
< 0.1%
-87.52527448 10
< 0.1%
-87.52540287 3
 
< 0.1%
Distinct112164
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-07-16T21:38:12.860609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.780603
Min length24

Characters and Unicode

Total characters6288533
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77171 ?
Unique (%)35.3%

Sample

1st row(41.75046233, -87.610058986)
2nd row(41.896293625, -87.624279611)
3rd row(41.911482929, -87.744185282)
4th row(41.953552585, -87.647905229)
5th row(41.881972634, -87.731728591)
ValueCountFrequency (%)
41.976290414 284
 
0.1%
87.905227221 284
 
0.1%
41.883500187 261
 
0.1%
87.627876698 261
 
0.1%
41.884335468 253
 
0.1%
87.619164088 253
 
0.1%
41.788987036 208
 
< 0.1%
87.74147999 208
 
< 0.1%
41.930906002 189
 
< 0.1%
87.644017035 189
 
< 0.1%
Other values (224259) 434608
99.5%
2023-07-16T21:38:13.699774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 699785
11.1%
8 648352
10.3%
4 565404
9.0%
1 559101
 
8.9%
6 498280
 
7.9%
. 436998
 
6.9%
9 403692
 
6.4%
5 376618
 
6.0%
2 355704
 
5.7%
3 346226
 
5.5%
Other values (6) 1398373
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4759040
75.7%
Other Punctuation 655497
 
10.4%
Open Punctuation 218499
 
3.5%
Space Separator 218499
 
3.5%
Dash Punctuation 218499
 
3.5%
Close Punctuation 218499
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 699785
14.7%
8 648352
13.6%
4 565404
11.9%
1 559101
11.7%
6 498280
10.5%
9 403692
8.5%
5 376618
7.9%
2 355704
7.5%
3 346226
7.3%
0 305878
6.4%
Other Punctuation
ValueCountFrequency (%)
. 436998
66.7%
, 218499
33.3%
Open Punctuation
ValueCountFrequency (%)
( 218499
100.0%
Space Separator
ValueCountFrequency (%)
218499
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 218499
100.0%
Close Punctuation
ValueCountFrequency (%)
) 218499
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6288533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 699785
11.1%
8 648352
10.3%
4 565404
9.0%
1 559101
 
8.9%
6 498280
 
7.9%
. 436998
 
6.9%
9 403692
 
6.4%
5 376618
 
6.0%
2 355704
 
5.7%
3 346226
 
5.5%
Other values (6) 1398373
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6288533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 699785
11.1%
8 648352
10.3%
4 565404
9.0%
1 559101
 
8.9%
6 498280
 
7.9%
. 436998
 
6.9%
9 403692
 
6.4%
5 376618
 
6.0%
2 355704
 
5.7%
3 346226
 
5.5%
Other values (6) 1398373
22.2%

Interactions

2023-07-16T21:37:49.511171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:26.922480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:29.783916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:32.432582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:35.288960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:38.114315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:41.396339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:44.245510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:47.004529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:49.792515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:27.242723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:30.069404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:32.703594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:35.576003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:38.540302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:41.722325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:44.558585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:47.273832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:50.076555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:27.572622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:30.373540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:32.970323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:35.835504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:38.851214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:42.029329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:44.869122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:47.524780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:50.340935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:27.885803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:30.691188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:33.268428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:36.121612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:39.159210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:42.330214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:45.181536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:47.827849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:50.625011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:28.181062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:30.986659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:33.549305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:36.401196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:39.457289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:42.663835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:45.456266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:48.121537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:50.930621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:28.521052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:31.291293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:33.848445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:36.766361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:39.834913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:43.022510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:45.809185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:48.391998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:51.186299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:28.819939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:31.585610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:34.141087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:37.087939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:40.181783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:43.331671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:46.137711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:48.673566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:51.499266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:29.159846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:31.902080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:34.503371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:37.461468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:40.746026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:43.661651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:46.448009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:48.968586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:51.777502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:29.484047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:32.167635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:34.838700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:37.799230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:41.076690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:43.960876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:46.708841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T21:37:49.264690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-16T21:38:13.945388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IDBeatDistrictWardCommunity AreaX CoordinateY CoordinateLatitudeLongitudePrimary TypeArrestDomesticFBI Code
ID1.0000.0130.0130.009-0.013-0.0040.0110.011-0.0040.9960.0280.0140.999
Beat0.0131.0000.9990.662-0.560-0.6140.6630.664-0.6090.0870.0500.1330.087
District0.0130.9991.0000.667-0.563-0.6210.6700.671-0.6160.0830.0450.1240.083
Ward0.0090.6620.6671.000-0.548-0.4900.6600.660-0.4840.0850.0550.1450.085
Community Area-0.013-0.560-0.563-0.5481.0000.328-0.814-0.8140.3160.0840.0550.1340.084
X Coordinate-0.004-0.614-0.621-0.4900.3281.000-0.549-0.5521.0000.0000.0000.0000.000
Y Coordinate0.0110.6630.6700.660-0.814-0.5491.0001.000-0.5370.0000.0000.0000.000
Latitude0.0110.6640.6710.660-0.814-0.5521.0001.000-0.5400.0000.0000.0000.000
Longitude-0.004-0.609-0.616-0.4840.3161.000-0.537-0.5401.0000.0000.0000.0000.000
Primary Type0.9960.0870.0830.0850.0840.0000.0000.0000.0001.0000.5480.5060.840
Arrest0.0280.0500.0450.0550.0550.0000.0000.0000.0000.5481.0000.0180.548
Domestic0.0140.1330.1240.1450.1340.0000.0000.0000.0000.5060.0181.0000.507
FBI Code0.9990.0870.0830.0850.0840.0000.0000.0000.0000.8400.5480.5071.000

Missing values

2023-07-16T21:37:52.299702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T21:37:53.641075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDCase NumberDateBlockIUCRPrimary TypeDescriptionLocationArrestDomesticBeatDistrictWardCommunity AreaFBI CodeX CoordinateY CoordinateYearUpdated OnLatitudeLongitudeLocation.1
012789250JF35106801-01-2022 00:00079XX S ST LAWRENCE AVE810THEFTOVER $500RESIDENCEFalseFalse6246644611816121852489202211-12-2022 15:4641.750462-87.610059(41.75046233, -87.610058986)
112783300JF34390801-01-2022 00:00007XX N MICHIGAN AVE580STALKINGSIMPLESTREETFalseFalse18331842808A11772931905597202211-12-2022 15:4641.896294-87.624280(41.896293625, -87.624279611)
212814497JF38096301-01-2022 00:00047XX W WABANSIA AVE1563SEX OFFENSECRIMINAL SEXUAL ABUSERESIDENCE - GARAGEFalseTrue25332537251711446101910885202211-12-2022 15:4641.911483-87.744185(41.911482929, -87.744185282)
312822046JF38950401-01-2022 00:00039XX N PINE GROVE AVE1153DECEPTIVE PRACTICEFINANCIAL IDENTITY THEFT OVER $ 300APARTMENTFalseFalse1925194661111706941926410202211-12-2022 15:4641.953553-87.647905(41.953552585, -87.647905229)
412836994JF40361301-01-2022 00:00042XX W WASHINGTON BLVD1750OFFENSE INVOLVING CHILDRENCHILD ABUSERESIDENCEFalseTrue111411282608B11480761900155202211-12-2022 15:4641.881973-87.731729(41.881972634, -87.731728591)
512840187JF41140901-01-2022 00:00046XX N SHERIDAN RD820THEFT$500 AND UNDERAPARTMENTFalseFalse191419463611687821931175202211-12-2022 15:4641.966670-87.654795(41.966669657, -87.654795172)
612849613JF42294601-01-2022 00:00023XX W DEVON AVE1153DECEPTIVE PRACTICEFINANCIAL IDENTITY THEFT OVER $ 300APARTMENTFalseFalse2413245021111593591942426202211-12-2022 15:4641.997742-87.689131(41.997742435, -87.689130952)
712895863JF47502301-01-2022 00:00112XX S CENTRAL PARK AVE1720OFFENSE INVOLVING CHILDRENCONTRIBUTE TO THE DELINQUENCY OF CHILDRESIDENCEFalseTrue22112219742011543821829582202211/20/2022 03:46:15 PM41.688186-87.710450(41.688186208, -87.710450203)
812584964JF10383401-01-2022 00:00070XX S SANGAMON ST1310CRIMINAL DAMAGETO PROPERTYAPARTMENTFalseFalse73376681411711901858171202210-11-2022 16:4541.766289-87.648084(41.766288528, -87.648084026)
912586516JF10579201-01-2022 00:00007XX W 81ST ST1310CRIMINAL DAMAGETO PROPERTYRESIDENCEFalseFalse622621711411729121851164202211-12-2022 15:4641.747023-87.641979(41.747022627, -87.641978681)
IDCase NumberDateBlockIUCRPrimary TypeDescriptionLocationArrestDomesticBeatDistrictWardCommunity AreaFBI CodeX CoordinateY CoordinateYearUpdated OnLatitudeLongitudeLocation.1
21848912915906JF50220312-07-2022 23:40002XX W 24TH ST031AROBBERYARMED - HANDGUNSTREETFalseFalse91492534311751141888397202212/14/2022 03:49:33 PM41.849145-87.632798(41.849144867, -87.632798144)
21849012915866JF50224112-07-2022 23:40079XX S ASHLAND AVE1121DECEPTIVE PRACTICECOUNTERFEITING DOCUMENTSTREETTrueFalse611621711011670511852166202212/14/2022 03:49:33 PM41.749899-87.663426(41.749899475, -87.663426378)
21849112916907JF50292112-07-2022 23:45118XX S WALLACE ST910MOTOR VEHICLE THEFTAUTOMOBILESTREETFalseFalse52453453711744351826477202212/14/2022 03:49:33 PM41.679244-87.637129(41.679244262, -87.637129451)
21849212915934JF50226712-07-2022 23:49075XX N PAULINA ST1020ARSONBY FIRECTA PLATFORMTrueFalse242224491911636901950023202212/14/2022 03:49:33 PM42.018498-87.672983(42.018498254, -87.672983233)
21849312915935JF50219412-07-2022 23:49011XX W GRANVILLE AVE1020ARSONBY FIRECTA PLATFORMTrueFalse2433244877911675431941332202212/14/2022 03:49:33 PM41.994568-87.659057(41.994567578, -87.65905678)
21849412915874JF50216912-07-2022 23:50040XX W 59TH ST860THEFTRETAIL THEFTTAVERN / LIQUOR STOREFalseFalse81382365611506291865174202212/14/2022 03:49:33 PM41.785931-87.723266(41.785930664, -87.723266186)
21849512915875JF50217112-07-2022 23:51044XX S DREXEL BLVD496BATTERYAGGRAVATED DOMESTIC BATTERY - KNIFE / CUTTING INSTRUMENTAPARTMENTFalseTrue221243904B11829361875860202212/14/2022 03:49:33 PM41.814564-87.604481(41.814563821, -87.604481355)
21849612917956JF50218512-07-2022 23:52009XX N LAWNDALE AVE486BATTERYDOMESTIC BATTERY SIMPLESTREETFalseTrue111211272308B11515461905984202212/14/2022 03:49:33 PM41.897901-87.718833(41.897900553, -87.718833412)
21849712915844JF50218012-07-2022 23:58013XX W 73RD ST2091NARCOTICSFORFEIT PROPERTYSTREETTrueFalse73476671811685781856355202212/14/2022 03:49:33 PM41.761362-87.657710(41.761361892, -87.657710237)
21849812915894JF50221512-07-2022 23:59012XX S HARDING AVE4310OTHER OFFENSEPOSSESSION OF BURGLARY TOOLSABANDONED BUILDINGTrueFalse10111024292611502441894089202212/14/2022 03:49:33 PM41.865285-87.723926(41.865284862, -87.723925799)

Duplicate rows

Most frequently occurring

IDCase NumberDateBlockIUCRPrimary TypeDescriptionLocationArrestDomesticBeatDistrictWardCommunity AreaFBI CodeX CoordinateY CoordinateYearUpdated OnLatitudeLongitudeLocation.1# duplicates
012582274JF10055301-01-2022 12:30005XX N RUSH ST910MOTOR VEHICLE THEFTAUTOMOBILESTREETFalseFalse183418428711770041904147202210-11-2022 16:4541.892321-87.625385(41.892321303, -87.625384982)2
112582764JF10056401-01-2022 12:30063XX N FRANCISCO AVE486BATTERYDOMESTIC BATTERY SIMPLEAPARTMENTFalseTrue24132450208B11558591941891202210-11-2022 16:4541.996346-87.702021(41.996345906, -87.702020727)2